Generative Engine Optimisation

GEO is the practice of managing how a person or organisation is portrayed in AI-generated responses across systems such as ChatGPT, Perplexity, Claude, Google AI Overviews, and Gemini. While traditional SEO focuses on search engine rankings, GEO targets the accuracy and sentiment of AI-generated narratives. As AI becomes a primary source of information, GEO is now a vital part of any modern reputation strategy.

AI language models are trained on vast amounts of web text, including news, Wikipedia, forum posts and other indexed content. The model synthesises this data into a narrative. Content that is authoritative, frequently cited and clearly attributed is more likely to be used. Conversely, negative or inaccurate information in the training data will appear in AI responses unless it is actively managed.

Traditional ORM focuses on search engine results that people see when they Google your name. GEO addresses a different layer: what AI systems say when asked about you directly. The two are linked but distinct. A clean Google result does not guarantee a clean AI narrative, as AI models are often trained on historical data that may no longer appear in current search rankings. Both require active management.

AI models are updated on varying cycles - some continuously, some quarterly or annually. Changes to the underlying web content that informs AI training take time to propagate into model responses. Pavesen works across both the immediate layer (influencing what AI systems surface through RAG and live search integration) and the longer-term training layer (building authoritative content that will be incorporated into future model updates).

We cover ChatGPT (OpenAI), Perplexity, Claude (Anthropic), Google AI Overviews, Gemini, Microsoft Copilot and other frontier models. Each system uses different source weighting and update cycles, which requires a platform-specific monitoring strategy.

Yes, though the process is different from requesting a Google removal. AI platforms have feedback and correction channels, but the most lasting solution is ensuring that accurate, authoritative source content is clearly indexed. This ensures AI systems have the correct information to draw from. Pavesen manages both the immediate correction layer and the underlying content that informs AI training.